The generalized smooth transition autoregression (GSTAR) parametrizes the joint asymmetry in the duration and length of cycles in macroeconomic time series by using particular generalizations of the logistic function. The symmetric smooth transition and linear autoregressions are nested in the GSTAR. A test for the null hypothesis of dynamic symmetry is presented. Two case studies indicate that dynamic asymmetry is a key feature of the U.S. economy. The GSTAR model beats its competitors for point forecasting, but this superiority becomes less evident for density forecasting and in uncertain forecasting environments.

Forecasting dynamically asymmetric fluctuations of the U.S. business cycle / Zanetti Chini, Emilio. - In: INTERNATIONAL JOURNAL OF FORECASTING. - ISSN 0169-2070. - 34:4(2018), pp. 711-732. [10.1016/j.ijforecast.2018.05.003]

Forecasting dynamically asymmetric fluctuations of the U.S. business cycle

Zanetti Chini, Emilio
2018

Abstract

The generalized smooth transition autoregression (GSTAR) parametrizes the joint asymmetry in the duration and length of cycles in macroeconomic time series by using particular generalizations of the logistic function. The symmetric smooth transition and linear autoregressions are nested in the GSTAR. A test for the null hypothesis of dynamic symmetry is presented. Two case studies indicate that dynamic asymmetry is a key feature of the U.S. economy. The GSTAR model beats its competitors for point forecasting, but this superiority becomes less evident for density forecasting and in uncertain forecasting environments.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1283860
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